Exploring Chinese postgraduates’ acceptance of GenAI-assisted interpreting learning: insights from an SEM-fsQCA analysis

Generative artificial intelligence (GenAI) shows promise in interpreter training, yet understanding the factors influencing interpreting students’ intentions to use GenAI remains underexplored. Drawing on the technology acceptance model and task-technology fit theory, this study examined the acceptance of GenAI-assisted interpreting among 211 Chinese postgraduate students. Data were collected via a web-based questionnaire and analysed using a dual approach: structural equation modelling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA). The findings suggested that students who frequently used GenAI, whether for general or interpreting-specific tasks, were more willing to adopt it in future interpreting tasks. SEM results indicated that their use intention was predominantly driven by perceived usefulness, with GenAI fit exerting a stronger effect on perceived usefulness than that of perceived ease of use. The fsQCA analysis revealed five configurations leading to use intention, highlighting perceived usefulness and GenAI fit as core conditions. Although GenAI fit did not directly predict use intention in SEM, fsQCA showed that it could compensate for unclear task demands or weak technology features, extending task-technology fit into a flexible, context-responsive concept. These insights highlighted the value of matching GenAI capacities with interpreting tasks to improve students’ technology acceptance, thus supporting the development of technology-augmented interpreters.